# Pose2Gait: Extracting Gait Features from Monocular Video of Individuals with Dementia

arXiv: https://arxiv.org/abs/2308.11484 · HTML: https://mlsanity.com/paper/2308.11484/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 22 Aug 2023.

## Builds on (most cited first, 6 of 6)

- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time](https://mlsanity.com/paper/2211.03375/) (2022)
- [Recent Advances in Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective](https://mlsanity.com/paper/2104.11536/) (2021)
- [Estimating Parkinsonism Severity in Natural Gait Videos of Older Adults with Dementia](https://mlsanity.com/paper/2105.03464/) (2021)
- [Transforming Gait: Video-Based Spatiotemporal Gait Analysis](https://mlsanity.com/paper/2203.09371/) (2022)
- [Single Person Pose Estimation: A Survey](https://mlsanity.com/paper/2109.10056/) (2021)

## Built on by (most cited first, 2 of 2)

- [Benchmarking Skeleton-based Motion Encoder Models for Clinical Applications: Estimating Parkinson's Disease Severity in Walking Sequences](https://mlsanity.com/paper/2405.17817/) (2024)
- [SynthGait-19K: A Physically Grounded Synthetic Video Dataset for Gait Parameter Estimation](https://mlsanity.com/paper/2609.08108/) (2026)

## Abstract (arXiv)

Video-based ambient monitoring of gait for older adults with dementia has the potential to detect negative changes in health and allow clinicians and caregivers to intervene early to prevent falls or hospitalizations. Computer vision-based pose tracking models can process video data automatically and extract joint locations; however, publicly available models are not optimized for gait analysis on older adults or clinical populations. In this work we train a deep neural network to map from a two dimensional pose sequence, extracted from a video of an individual walking down a hallway toward a wall-mounted camera, to a set of three-dimensional spatiotemporal gait features averaged over the walking sequence. The data of individuals with dementia used in this work was captured at two sites using a wall-mounted system to collect the video and depth information used to train and evaluate our model. Our Pose2Gait model is able to extract velocity and step length values from the video that are correlated with the features from the depth camera, with Spearman's correlation coefficients of .83 and .60 respectively, showing that three dimensional spatiotemporal features can be predicted from monocular video. Future work remains to improve the accuracy of other features, such as step time and step width, and test the utility of the predicted values for detecting meaningful changes in gait during longitudinal ambient monitoring.